Enhancing Turbine Performance Degradation Prediction with Atmospheric Factors
##plugins.themes.bootstrap3.article.main##
##plugins.themes.bootstrap3.article.sidebar##
Abstract
Heavy duty gas turbine engines are not only ingesting the air, but also eating a myriad of aerosol particles, which may have various negative effects on the turbine operation efficiency as well as the component failure. This paper attempts to develop predictive degradation models for gas turbines by integrating satellite collected atmospheric factors, on-site monitoring data, and physics-based calculated performance results. Multiple variables are analyzed and employed for predictive modeling. The vital variables are identified by using data exploratory correlation analysis and stepwise regression analysis. The performance degradation calculation is obtained from physics based thermodynamic heat balance of gas turbine. It requires balancing mass and energy of gas turbine to match measurement data through thermodynamic cycle matching. The performance degradation prior to the offline water wash is used as the predictor. Artificial neural network modeling is employed to establish the predictive models. A procedure is presented to explain the proposed methodology, and results are discussed. This paper provides an effective methodology and procedure to apply big data for the performance degradation prediction of gas turbines.
How to Cite
##plugins.themes.bootstrap3.article.details##
diagnostic performance, neural network, gas turbine, predictive analytics, degradation
Balevic D, Hartman S and Youmans R (2010), Heavy-Duty Gas Turbine Operating and Maintenance Considerations. GER-3620L.1, GE Energy, Atlanta, GA.
Brooks FJ (2000), GE Gas Turbine Performance Characteristics. GER-3567H. GE Power Systems, Schenectady, NY.
Jiang X, Foster C (2013), “Remote Thermal Performance Monitoring and Diagnostics – Turning Data into Knowledge,” Proceedings of the ASME 2013 Power Conference, July 29 – August 1, 2013, Boston, Massachusetts, USA.
Jiang X, Foster C (2014), “Plant Performance Monitoring and Diagnostics – Remote, Real-Time and Automation,” Proceedings of ASME Turbo Expo 2014: Turbine Technical Conference and Exposition, June 16 – 20, 2014, Düsseldorf, Germany.
Johnston JR (2000), Performance and Reliability Improvements for Heavy-Duty Gas Turbines. GER-3571H, GE Power Systems, Schenectady, NY.
Meher-Homji CB, Chaker MA and Motiwala HM (2001), “Gas Turbine Performance Deterioration,” Proceedings of the 30th Turbomachinery Symposium, 139-175, Houston, TX.
Tukey, JW (1977). Exploratory Data Analysis. Addison-Wesley, Reading, MA.
The Prognostic and Health Management Society advocates open-access to scientific data and uses a Creative Commons license for publishing and distributing any papers. A Creative Commons license does not relinquish the author’s copyright; rather it allows them to share some of their rights with any member of the public under certain conditions whilst enjoying full legal protection. By submitting an article to the International Conference of the Prognostics and Health Management Society, the authors agree to be bound by the associated terms and conditions including the following:
As the author, you retain the copyright to your Work. By submitting your Work, you are granting anybody the right to copy, distribute and transmit your Work and to adapt your Work with proper attribution under the terms of the Creative Commons Attribution 3.0 United States license. You assign rights to the Prognostics and Health Management Society to publish and disseminate your Work through electronic and print media if it is accepted for publication. A license note citing the Creative Commons Attribution 3.0 United States License as shown below needs to be placed in the footnote on the first page of the article.
First Author et al. This is an open-access article distributed under the terms of the Creative Commons Attribution 3.0 United States License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.